2020-06-03 08:34:20 +02:00
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import pandas as pd
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import numpy as np
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from pprint import pprint
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import dataset
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import random
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# obliczenie entropii dla wskazanej kolumny
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2020-06-03 10:58:32 +02:00
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def entropy(attribute):
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values, counts = np.unique(attribute, return_counts=True)
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2020-06-03 08:34:20 +02:00
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entropy = np.sum(
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[(-counts[i] / np.sum(counts)) * np.log2(counts[i] / np.sum(counts)) for i in range(len(values))])
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return entropy
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#obliczanie wartości przyrostu informacji
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2020-06-03 10:58:32 +02:00
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def info_gain(data, split_attribute, target):
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2020-06-03 08:34:20 +02:00
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# Wartość entropii zbioru
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2020-06-03 10:58:32 +02:00
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_entropy = entropy(data[target])
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2020-06-03 08:34:20 +02:00
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2020-06-03 10:58:32 +02:00
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# Wyodrębnienie poszczególnych podzbiorów
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vals, counts = np.unique(data[split_attribute], return_counts=True)
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2020-06-03 08:34:20 +02:00
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# Średnia ważona entropii każdego podzbioru
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weighted_entropy = np.sum(
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2020-06-03 10:58:32 +02:00
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[(counts[i] / np.sum(counts)) * entropy(data.where(data[split_attribute] == vals[i]).dropna()[target])
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2020-06-03 08:34:20 +02:00
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for i in range(len(vals))])
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# Przyrost informacji
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2020-06-03 10:58:32 +02:00
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information_gain = _entropy - weighted_entropy
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2020-06-03 08:34:20 +02:00
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return information_gain
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2020-06-03 10:58:32 +02:00
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def ID3(data, original_data, attributes, target, parent_node_class=None):
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2020-06-03 08:34:20 +02:00
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# Jeżeli wszystkie atrybuty są takie same, zwracamy liść z pierwszą napotkaną wartością
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2020-06-03 10:58:32 +02:00
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if len(np.unique(data[target])) <= 1:
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return np.unique(data[target])[0]
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2020-06-03 08:34:20 +02:00
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elif len(data) == 0:
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2020-06-03 10:58:32 +02:00
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return np.unique(original_data[target])[
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np.argmax(np.unique(original_data[target], return_counts=True)[1])]
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2020-06-03 08:34:20 +02:00
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2020-06-03 10:58:32 +02:00
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elif len(attributes) == 0:
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2020-06-03 08:34:20 +02:00
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return parent_node_class
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else:
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2020-06-03 10:58:32 +02:00
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# nadrzędna wartość
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parent_node_class = np.unique(data[target])[
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np.argmax(np.unique(data[target], return_counts=True)[1])]
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2020-06-03 08:34:20 +02:00
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2020-06-03 10:58:32 +02:00
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# obliczenie przyrostu informacji dla każdego atrybutu
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item_values = [info_gain(data, i, target) for i in
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attributes]
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2020-06-03 08:34:20 +02:00
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2020-06-03 10:58:32 +02:00
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# Wybór najlepszego atrybutu
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best_attribute_index = np.argmax(item_values)
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best_attribute = attributes[best_attribute_index]
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2020-06-03 08:34:20 +02:00
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# Struktura drzewa
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2020-06-03 10:58:32 +02:00
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tree = {best_attribute: {}}
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2020-06-03 08:34:20 +02:00
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# Aktualizacja zbioru atrybutów
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2020-06-03 10:58:32 +02:00
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attributes = [i for i in attributes if i != best_attribute]
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2020-06-03 08:34:20 +02:00
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2020-06-03 10:58:32 +02:00
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# Budowa poddrzewa dla każdej wartości wybranego atrybutu
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for value in np.unique(data[best_attribute]):
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2020-06-03 08:34:20 +02:00
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2020-06-03 10:58:32 +02:00
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sub_data = data.where(data[best_attribute] == value).dropna()
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subtree = ID3(sub_data, data, attributes, target, parent_node_class)
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2020-06-03 08:34:20 +02:00
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2020-06-03 10:58:32 +02:00
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tree[best_attribute][value] = subtree
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2020-06-03 08:34:20 +02:00
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return (tree)
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#tesownie drzewa
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def test(data, tree):
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queries = data.iloc[:, :-1].to_dict(orient="records")
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predicted = pd.DataFrame(columns=["predicted"])
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for i in range(len(data)):
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predicted.loc[i, "predicted"] = search(queries[i], tree, 'nie')
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print('Precyzja przewidywań: ', (np.sum(predicted["predicted"] == data['czy_chce_pracowac']) / len(data)) * 100, '%')
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2020-06-03 10:58:32 +02:00
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#dostowanie danych (lista na słownik) i wywolanie na nich funkcji serach
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def data_to_dict(data, tree):
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2020-06-03 08:34:20 +02:00
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queries = pd.DataFrame(data=data, columns=dataset.header)
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predicted = pd.DataFrame(columns=["predicted"])
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dict = queries.iloc[:, :-1].to_dict(orient="records")
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for i in range(len(data)):
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predicted.loc[i, "predicted"] = search(dict[i], tree, 'nie')
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predicted_list = predicted.values.tolist()
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return predicted_list[0][0]
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#przeszukwianie drzewa
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def search(query, tree, default='nie'):
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for key in list(query.keys()):
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if key in list(tree.keys()):
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try:
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result = tree[key][query[key]]
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except:
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return default
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result = tree[key][query[key]]
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if isinstance(result, dict):
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return search(query, result)
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else:
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return result
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2020-05-03 17:13:59 +02:00
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class main():
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def __init__(self,traktor,field,ui,path):
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self.traktor = traktor
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self.field = field
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self.ui = ui
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self.path = path
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2020-06-09 14:34:55 +02:00
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self.result = 0
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2020-05-03 17:13:59 +02:00
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def main(self):
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2020-06-03 08:34:20 +02:00
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training_data = pd.DataFrame(data=dataset.training_data, columns=dataset.header)
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testing_data = pd.DataFrame(data=dataset.testing_data, columns=dataset.header)
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2020-06-03 10:58:32 +02:00
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# Utworzenie drzewa
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2020-06-03 08:34:20 +02:00
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tree = ID3(training_data, training_data, training_data.columns[:-1], 'czy_chce_pracowac')
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pprint(tree)
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2020-06-03 10:58:32 +02:00
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# Testowanie drzewa
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2020-06-09 14:34:55 +02:00
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#print(test(testing_data, tree))
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2020-06-03 10:58:32 +02:00
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# Uzyskanie danych od agenta
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2020-06-03 08:34:20 +02:00
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ocena_burakow = self.ocen_ile_burakow()
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ocena_chwastow = self.ocen_ile_chwastow()
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2020-06-05 18:05:01 +02:00
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pogoda = self.field.get_pogoda_name()
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2020-06-03 08:34:20 +02:00
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print('chwasty: ' + ocena_chwastow)
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print('buraki: ' + ocena_burakow)
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print('pogoda: ' + pogoda)
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data = [[pogoda, ocena_chwastow, ocena_burakow, '']]
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#podjecie decyzji
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2020-06-09 14:34:55 +02:00
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self.result = data_to_dict(data, tree)
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print('czy placa sie pracowac: ' + self.result)
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def get_result(self):
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return self.result
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2020-06-03 10:58:32 +02:00
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2020-06-03 08:34:20 +02:00
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def licz_chwasty_buraki(self):
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chwasty = 0
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2020-06-03 10:58:32 +02:00
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buraki = 0
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2020-06-03 08:34:20 +02:00
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for i in self.field.field_matrix:
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for j in i:
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if(j==8):
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buraki = buraki + 1
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elif(j%2==1):
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chwasty = chwasty + 1
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return chwasty, buraki
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2020-06-03 10:58:32 +02:00
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2020-06-03 08:34:20 +02:00
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def ocen_ile_burakow(self):
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chwasty, buraki = self.licz_chwasty_buraki()
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if buraki < 5:
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return 'bardzo_malo'
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elif buraki >= 5 and buraki<10:
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return 'malo'
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elif buraki >=10 and buraki<15:
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return 'srednio'
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elif buraki >=15 and buraki<20:
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return 'duzo'
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elif buraki >=20:
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return 'bardzo_duzo'
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def ocen_ile_chwastow(self):
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chwasty, buraki = self.licz_chwasty_buraki()
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if chwasty < 40:
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return 'bardzo_malo'
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elif chwasty >= 40 and chwasty<42:
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return 'malo'
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elif chwasty >=42 and chwasty<45:
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return 'srednio'
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elif chwasty >=45 and chwasty<48:
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return 'duzo'
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elif chwasty >=48:
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return 'bardzo_duzo'
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2020-06-03 10:58:32 +02:00
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